research-article

Scaling PageRank to 100 Billion Pages

Author:

Stergios StergiouAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2761 - 2767

https://doi.org/10.1145/3366423.3380035

Published: 20 April 2020 Publication History

Abstract

Distributed graph processing frameworks formulate tasks as sequences of supersteps within which communication is performed asynchronously by sending messages over the graph edges. PageRank’s communication pattern is identical across all its supersteps since each vertex sends messages to all its edges. We exploit this pattern to develop a new communication paradigm that allows us to exchange messages that include only edge payloads, dramatically reducing bandwidth requirements. Experiments on a web graph of 38 billion vertices and 3.1 trillion edges yield execution times of 34.4 seconds per iteration, suggesting more than an order of magnitude improvement over the state-of-the-art.

References

[1]

2016. Common Crawl. http://commoncrawl.org/.

[2]

2016. Twitter User Graph. http://konect.uni-koblenz.de/networks/twitter_mpi.

[3]

2016. Web Data Commons. http://webdatacommons.org/.

[4]

2017. Apache Giraph. http://giraph.apache.org/.

[5]

2020. Facebook Company Info. http://newsroom.fb.com/company-info/.

[6]

2020. How Search organizes information. https://www.google.com/search /howsearchworks/crawling-indexing/.

[7]

Paolo Boldi, Massimo Santini, and Sebastiano Vigna. 2008. A Large Time-Aware Graph. SIGIR Forum 42, 2 (2008), 33–38.

Digital Library

[8]

RobertS. Boyer and J.Strother Moore. 1991. MJRTY-A Fast Majority Vote Algorithm. In Automated Reasoning, RobertS. Boyer (Ed.). Automated Reasoning Series, Vol. 1. Springer Netherlands, 105–117.

[9]

Yingyi Bu, Bill Howe, Magdalena Balazinska, and Michael D Ernst. 2010. HaLoop: Efficient iterative data processing on large clusters. Proceedings of the VLDB Endowment 3, 1-2 (2010), 285–296.

Digital Library

[10]

Aydın Buluç and John R Gilbert. 2011. The Combinatorial BLAS: Design, implementation, and applications. The International Journal of High Performance Computing Applications 25, 4(2011), 496–509.

Digital Library

[11]

Rong Chen, Jiaxin Shi, Yanzhe Chen, and Haibo Chen. 2015. Powerlyra: Differentiated graph computation and partitioning on skewed graphs. In Proceedings of the Tenth European Conference on Computer Systems. ACM, 1.

Digital Library

[12]

Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, and Sambavi Muthukrishnan. 2015. One Trillion Edges: Graph Processing at Facebook-Scale. Proceedings of the VLDB Endowment 8, 12 (2015).

Digital Library

[13]

Graham Cormode and Marios Hadjieleftheriou. 2008. Finding Frequent Items in Data Streams. Proceedings of the VLDB Endowment 1, 2 (2008), 1530–1541.

Digital Library

[14]

JE Gonzalez, Y Low, H Gu, D Bickson, and C Guestrin. 2012. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs.OSDI (2012).

[15]

JE Gonzalez, RS Xin, and A Dave. [n.d.]. GraphX: Graph Processing in a Distributed Dataflow Framework. In OSDI’14 Proceedings of the 11th USENIX conference on Operating Systems Design and Implementation.

[16]

Minyang Han, Khuzaima Daudjee, Khaled Ammar, M. Tamer Özsu, Xingfang Wang, and Tianqi Jin. 2014. An experimental comparison of pregel-like graph processing systems. Proceedings of the VLDB Endowment 7, 12 (Aug. 2014), 1047–1058. https://doi.org/10.14778/2732977.2732980

Digital Library

[17]

Wassily Hoeffding. 1963. Probability Inequalities for Sums of Bounded Random Variables. Journal of the American Statistical Association 58, 301(1963), 13–30.

[18]

Shan Jiang, Yuening Hu, Changsung Kang, Tim Daly Jr, Dawei Yin, Yi Chang, and Chengxiang Zhai. 2016. Learning Query and Document Relevance from a Web-scale Click Graph. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 185–194.

Digital Library

[19]

U Kang, Charalampos E Tsourakakis, and Christos Faloutsos. 2009. Pegasus: A peta-scale graph mining system implementation and observations. In Data Mining, 2009. ICDM’09. Ninth IEEE International Conference on. IEEE, 229–238.

Digital Library

[20]

Zuhair Khayyat, Karim Awara, Amani Alonazi, Hani Jamjoom, Dan Williams, and Panos Kalnis. 2013. Mizan: a system for dynamic load balancing in large-scale graph processing. In Proceedings of the 8th ACM European Conference on Computer Systems. ACM, 169–182.

Digital Library

[21]

Aapo Kyrola, Guy E Blelloch, Carlos Guestrin, 2012. GraphChi: Large-Scale Graph Computation on Just a PC. In OSDI, Vol. 12. 31–46.

[22]

AN Langville and CD Meyer. 2011. Google’s PageRank and beyond: The science of search engine rankings.

[23]

Yibei Ling and Wei Sun. 1992. A Supplement to Sampling-based Methods for Query Size Estimation in a Database System. SIGMOD Record 21, 4 (Dec. 1992), 12–15.

Digital Library

[24]

G Malewicz, MH Austern, and AJC Bik. 2010. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data.

Digital Library

[25]

Derek G Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, and Martín Abadi. 2013. Naiad: a timely dataflow system. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. ACM, 439–455.

Digital Library

[26]

Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab (1999).

[27]

Amitabha Roy, Laurent Bindschaedler, Jasmina Malicevic, and Willy Zwaenepoel. 2015. Chaos: Scale-out graph processing from secondary storage. In Proceedings of the 25th Symposium on Operating Systems Principles. ACM, 410–424.

Digital Library

[28]

Amitabha Roy, Ivo Mihailovic, and Willy Zwaenepoel. 2013. X-Stream: edge-centric graph processing using streaming partitions. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles. ACM, 472–488.

Digital Library

[29]

Semih Salihoglu and Jennifer Widom. 2013. GPS: a graph processing system. In Proceedings of the 25th International Conference on Scientific and Statistical Database Management - SSDBM. ACM Press, 1. https://doi.org/10.1145/2484838.2484843

Digital Library

[30]

Stergios Stergiou, Dipen Rughwani, and Kostas Tsioutsiouliklis. 2018. Shortcutting label propagation for distributed connected components. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 540–546.

Digital Library

[31]

Stergios Stergiou, Zygimantas Straznickas, Rolina Wu, and Kostas Tsioutsiouliklis. [n.d.]. Distributed Negative Sampling For Word Embeddings. In AAAI’17 Thirty-First AAAI Conference on Artificial Intelligence.

[32]

Yuanyuan Tian, Andrey Balmin, Severin Andreas Corsten, Shirish Tatikonda, and John McPherson. 2013. From think like a vertex to think like a graph. Proceedings of the VLDB Endowment 7, 3 (2013), 193–204.

Digital Library

[33]

Da Yan, James Cheng, Yi Lu, and Wilfred Ng. 2014. Blogel: A block-centric framework for distributed computation on real-world graphs. Proceedings of the VLDB Endowment 7, 14 (2014), 1981–1992.

Digital Library

[34]

Da Yan, James Cheng, Yi Lu, and Wilfred Ng. 2015. Effective techniques for message reduction and load balancing in distributed graph computation. In Proceedings of the 24th International Conference on World Wide Web. 1307–1317.

Digital Library

[35]

H Zhao and J Canny. 2014. Kylix: A Sparse Allreduce for Commodity Clusters. In 43rd International Conference on Parallel Processing (ICPP).

Cited By

Kang SFender AEaton JRees B(2020)Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286216(1-4)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286216

Index Terms

Scaling PageRank to 100 Billion Pages

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

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Published: 20 April 2020

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WWW '20

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

Kang SFender AEaton JRees B(2020)Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286216(1-4)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286216

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